Search Results (2,504)

Background/Objectives: The effect of metformin on the secretory function of thyrotropic cells is sex-dependent. The current study aimed to investigate whether the impact of this drug on activity of the hypothalamic–pituitary–thyroid axis in women is impacted by the androgen status of patients. Methods: The study population included 48 levothyroxine-naïve reproductive-aged women with subclinical hypothyroidism and prediabetes receiving 3.0 g of metformin daily. Women with (n = 24) and without (n = 24) polycystic ovary syndrome were matched for age, insulin sensitivity, TSH, and reasons for thyroid hypofunction. Circulating levels of glucose, glycated hemoglobin, insulin, TSH, thyroid hormones, gonadotropins, androgens, estradiol, SHBG, prolactin, ACTH, and IGF-1 were measured before metformin treatment and six months later. Results: At entry, women with and without polycystic ovary syndrome differed in LH, LH/FSH ratio, androgens, and estradiol. The decrease in TSH, fasting glucose and glycated hemoglobin, and the improvement in insulin sensitivity were less pronounced in women with than in women without polycystic ovary syndrome. In each group, there were no differences in the impact on TSH and thyroid hormones between patients with subclinical hypothyroidism of autoimmune and non-autoimmune origin. The changes in TSH inversely correlated with total testosterone and free androgen index. Only in women with coexisting polycystic ovary syndrome, did metformin slightly reduce LH, LH/FSH ratio, testosterone, and free androgen index. Conclusions: The results suggest that concurrent polycystic ovary syndrome attenuates metformin action on TSH secretion, which can be explained by increased androgen production. Moreover, the drug seems to alleviate PCOS-associated changes in the activity of the reproductive axis. Full article

Background/Objectives: Chemically or genetically detoxified pertussis toxin (PTx) is a crucial antigen component of the acellular pertussis vaccine. Chemical detoxification using glutaraldehyde generally causes significant structural changes to the toxin. However, how these structural changes in PTx affect its antigenic properties remains unclear. Additionally, there is limited knowledge regarding how many alterations in antigenic properties impact immunogenicity. Methods: To investigate the impact of structural changes on antigenic properties, we developed a sandwich ELISA to quantify the neutralizing epitopes on PTx. Subsequently, we analyzed different PTx toxoid (PTd) preparations with the assay. Additionally, we assessed the immunogenicity of various acellular pertussis vaccine candidates containing these PTd preparations. Finally, the assay was applied to evaluate the consistency of commercial batches of PTx and PTd intermediates. Results: The assay demonstrated reasonable specificity, accuracy, and precision, and it was sensitive enough to quantify variations in neutralizing epitopes among different PTd samples that shared the same protein concentration. Importantly, we found a positive correlation between the number of neutralizing epitopes in detoxified PTx and its immunogenicity, indicating that the amount of neutralizing epitopes present determines the immunogenicity of glutaraldehyde-inactivated PTx. Moreover, commercial batches of PTx and PTd intermediates exhibited minor variations in neutralizing epitopes. Conclusions: These findings have significant implications for developing acellular pertussis vaccines as they highlight the importance of preserving the neutralizing epitopes of PTx during detoxification to ensure the vaccine’s effectiveness. This assay is also valuable for the quality control of PTd as it more accurately represents the actual antigenic changes of PTx. Full article

Background and Objectives: this pilot study aimed to evaluate the diagnostic accuracy of ChatGPT-4o in analyzing oral mucosal lesions from clinical images. Materials and Methods: a total of 110 clinical images, including 100 pathological lesions and 10 healthy mucosal images, were retrieved from Google Images and analyzed by ChatGPT-4o using a standardized prompt. An expert panel of five clinicians established a reference diagnosis, categorizing lesions as benign or malignant. The AI-generated diagnoses were classified as correct or incorrect and further categorized as plausible or not plausible. The accuracy, sensitivity, specificity, and agreement with the expert panel were analyzed. The Artificial Intelligence Performance Instrument (AIPI) was used to assess the quality of AI-generated recommendations. Results: ChatGPT-4o correctly diagnosed 85% of cases. Among the 15 incorrect diagnoses, 10 were deemed plausible by the expert panel. The AI misclassified three malignant lesions as benign but did not categorize any benign lesions as malignant. Sensitivity and specificity were 91.7% and 100%, respectively. The AIPI score averaged 17.6 ± 1.73, indicating strong diagnostic reasoning. The McNemar test showed no significant differences between AI and expert diagnoses (p = 0.084). Conclusions: In this proof-of-concept pilot study, ChatGPT-4o demonstrated high diagnostic accuracy and strong descriptive capabilities in oral mucosal lesion analysis. A residual 8.3% false-negative rate for malignant lesions underscores the need for specialist oversight; however, the model shows promise as an AI-powered triage aid in settings with limited access to specialized care. Full article

Background/Objectives: Early experiences can significantly influence brain development, particularly when they occur during specific time windows known as sensitive or critical periods. Therefore, the early promotion of neurodevelopmental functions is crucial in children at risk for neurodevelopmental disabilities, such as those with cerebral palsy. This article introduces AMIRA (A Multidimensional and Integrated Rehabilitation Approach), a rehabilitative framework designed for infants at risk of neurodevelopmental disabilities. Methods: AMIRA is intended to guide clinical–rehabilitation reasoning rather than prescribe a rigid sequence of predetermined activities for the child. The theoretical foundation and structure of AMIRA are presented by formalizing its criteria, objectives, tools, and intervention procedures. The framework comprises four distinct sections, each supported by adaptive strategies to facilitate access to materials and to promote play-based interactions among the child, their environment, and communication partners. Particular attention is given to optimizing both micro- and macro-environments for children with, or at risk of, co-occurring visual impairment. Each rehabilitative section includes three progressive phases: an initial observation phase, a facilitation phase to support the child’s engagement, and an active experimentation phase that gradually introduces more challenging tasks. Results: The intervention pathways in AMIRA are organized according to six core developmental domains: behavioral–emotional self-regulation, visual function, postural–motor skills, praxis, interaction and communication, and cognitive function. These are outlined in structured charts that serve as flexible guidelines rather than prescriptive protocols. Each chart presents activities of increasing complexity aligned with typical developmental milestones up to 24 months of age. For each specific ability, the corresponding habilitation goals, contextual recommendations (including environmental setup, objects, and tools), and suggested activities are provided. Conclusions: This study presents a detailed intervention approach, offering both a practical framework and a structured set of activities for use in rehabilitative settings. Further studies will explore the efficacy of the proposed standardized approach. Full article

Background and Objectives: General-purpose multimodal large language models (LLMs) are increasingly used for medical image interpretation despite lacking clinical validation. This study evaluates the diagnostic reliability of ChatGPT-4o and Claude 2 in photographic assessment of adolescent idiopathic scoliosis (AIS) against radiological standards. This study examines two critical questions: whether families can derive reliable preliminary assessments from LLMs through analysis of clinical photographs and whether LLMs exhibit cognitive fidelity in their visuospatial reasoning capabilities for AIS assessment. Materials and Methods: A prospective diagnostic accuracy study (STARD-compliant) analyzed 97 adolescents (74 with AIS and 23 with postural asymmetry). Standardized clinical photographs (nine views/patient) were assessed by two LLMs and two orthopedic residents against reference radiological measurements. Primary outcomes included diagnostic accuracy (sensitivity/specificity), Cobb angle concordance (Lin’s CCC), inter-rater reliability (Cohen’s κ), and measurement agreement (Bland–Altman LoA). Results: The LLMs exhibited hazardous diagnostic inaccuracy: ChatGPT misclassified all non-AIS cases (specificity 0% [95% CI: 0.0–14.8]), while Claude 2 generated 78.3% false positives. Systematic measurement errors exceeded clinical tolerance: ChatGPT overestimated thoracic curves by +10.74° (LoA: −21.45° to +42.92°), exceeding tolerance by >800%. Both LLMs showed inverse biomechanical concordance in thoracolumbar curves (CCC ≤ −0.106). Inter-rater reliability fell below random chance (ChatGPT κ = −0.039). Universal proportional bias (slopes ≈ −1.0) caused severe curve underestimation (e.g., 10–15° error for 50° deformities). Human evaluators demonstrated superior bias control (0.3–2.8° vs. 2.6–10.7°) but suboptimal specificity (21.7–26.1%) and hazardous lumbar concordance (CCC: −0.123). Conclusions: General-purpose LLMs demonstrate clinically unacceptable inaccuracy in photographic AIS assessment, contraindicating clinical deployment. Catastrophic false positives, systematic measurement errors exceeding tolerance by 480–1074%, and inverse diagnostic concordance necessitate urgent regulatory safeguards under frameworks like the EU AI Act. Neither LLMs nor photographic human assessment achieve reliability thresholds for standalone screening, mandating domain-specific algorithm development and integration of 3D modalities. Full article

Biotin (vitamin B7) is a common, naturally occurring water-soluble vitamin. It belongs to the broad group of B vitamins. It is a common ingredient in dietary supplements, cosmetics, medicines, and parapharmaceutical preparations administered orally or applied topically (to the skin, hair, nails). The problem of the relationship between vitamin B supplementation and sensitivity seems to be multi-threaded. There is little literature data that would confirm that oral vitamin B supplementation or local exposure to biotin is a significant sensitizing factor. Moreover, it seems that allergy to vitamin B7 is very rare. It is possible, however, that the relationship between biotin and hypersensitivity is not limited to its direct action, but results from its essential metabolic function. Vitamin B7, as a cofactor of five carboxylases, affects the main pathways of cellular metabolism. Both deficiency and excess of biotin can result in metabolic disorders, which can have a significant impact on the homeostasis of the entire organism, including the efficient functioning of the immune system. Dysregulation of immune systems leads to its dysfunctional functioning, which can also lead to sensitization to various environmental antigens (allergens). Biotin is also used as an element of some methodological models in immunochemical tests (in vitro diagnostics), including methods used to measure the concentration of immunoglobulin E (IgE), both total (tIgE) and allergen-specific (sIgE). For this reason, vitamin B7 supplementation can be a significant interfering factor in some immunochemical tests, which can lead to false laboratory test results, both false positive and false negative, depending on the test format. This situation can have a direct impact on the quality and effectiveness of diagnostics in various clinical situations, including allergy diagnostics. This review focuses on the role of biotin in allergic reactions, both as a causative factor (allergen/hapten), a factor predisposing to the development of sensitization to various allergens, and an interfering factor in immunochemical methods used in laboratory diagnosis of hypersensitivity reactions and how it can be prevented. Full article

Current mainstream classification methods predominantly employ end-to-end multi-class frameworks. These approaches encounter inherent challenges including high-dimensional feature space complexity, decision boundary ambiguity that escalates with increasing class cardinality, sensitivity to label noise, and limited adaptability to dynamic model expansion. However, human beings may avoid these mistakes naturally. Research indicates that humans subconsciously employ a decision-making process favoring binary outcomes, particularly when responding to questions requiring nuanced differentiation. Intuitively, responding to binary inquiries such as “yes/no” often proves easier for humans than addressing queries of “what/which”. Inspired by the human decision-making hypothesis, we proposes a decision paradigm named the evolutionary binary decision framework (EBDF) centered around binary classification, evolving from traditional multi-classifiers in deep learning. To facilitate this evolution, we leverage the top-N outputs from the traditional multi-class classifier to dynamically steer subsequent binary classifiers, thereby constructing a cascaded decision-making framework that emulates the hierarchical reasoning of a binary decision tree. Theoretically, we demonstrate mathematical proof that by surpassing a certain threshold of the performance of binary classifiers, our framework may outperform traditional multi-classification framework. Furthermore, we conduct experiments utilizing several prominent deep learning models across various image classification datasets. The experimental results indicate significant potential for our strategy to surpass the ceiling in multi-classification performance. Full article

This paper examines the epistemological transformation prompted by the integration of generative artificial intelligence technologies into social science research, proposing the “adaptive epistemology” paradigm. In today’s post-digital era—characterized by pervasive infrastructures and non-human agents endowed with generative capabilities—traditional research approaches have become inadequate. Through a critical review of historical and discursive paradigms (positivism, interpretivism, critical realism, pragmatism, transformative paradigms, mixed and digital methods), here I show how the advent of digital platforms and large language models reconfigures the boundaries between data collection, analysis, and interpretation. Employing a theoretical–conceptual framework that draws on sociotechnical systems theory, platform studies, and the philosophy of action, the core features of adaptive epistemology are identified: dynamism, co-construction of meaning between researcher and system, and the capacity to generate methodological solutions in response to rapidly evolving contexts. The findings demonstrate the need for reasoning in terms of an adaptive epistemology that could offer a robust theoretical and methodological framework for guiding social science research in the post-digital society, emphasizing flexibility, reflexivity, and ethical sensitivity in the deployment of generative tools. Full article

Background/Objectives: Falls in hospitalized pediatric patients are frequent and can lead to serious complications and increased healthcare costs. Nurses typically assess fall risk using structured tools such as the Humpty Dumpty Fall Scale (HDFS), alongside nursing diagnoses such as Fall risk ND, which are based on clinical reasoning. However, the degree of alignment between the HDFS and the nursing reasoning-based diagnostic approach in assessing fall risk remains unclear. This study aims to assess the alignment between the HDFS and Fall risk ND in identifying fall risk among hospitalized pediatric patients. Methods: A retrospective observational study was conducted in a tertiary pediatric hospital in Italy, including all pediatric patients admitted in 2022. Fall risk was assessed within 24 h from hospital admission using two approaches, the HDFS (risk identified with the standard cutoff, score ≥ 12) and Fall risk ND, based on the nurse’s clinical reasoning and recorded through the PAIped clinical nursing information system. Discriminative performance was analyzed using receiver operating characteristic curve analysis. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. A confusion matrix evaluated classification performance at the cutoff (≥12). Results: Among 2086 inpatients, 80.9% had a recorded Fall risk ND. Of the 1853 patients assessed with the HDFS, 52.7% were classified as at risk (HDFS score ≥ 12). The HDFS showed low discriminative ability in detecting patients with a Fall risk ND (AUC = 0.568; 95% CI: 0.535−0.602). The PPV was high (85.1%), meaning that most patients identified as at risk by the HDFS were also judged to be at risk by nurses through Fall risk ND. However, the NPV was low (20.1%), indicating that many patients with low HDFS scores were still diagnosed with Fall risk ND by nurses. Conclusions: The HDFS shows limited ability to discriminate pediatric patients with Fall risk ND, capturing a risk profile that does not fully align with nursing clinical reasoning. This suggests that standardized tools and clinical reasoning address distinct yet complementary dimensions of fall risk assessment. Integrating the HDFS into a structured nursing diagnostic process—guided by clinical expertise and supported by continuous education—can strengthen the effectiveness of fall prevention strategies and enhance patient safety in pediatric settings. Full article

Carbon fiber-reinforced polymer (CFRP) laminates have been widely coated on aged and damaged structures for recovering or enhancing their structural performance. The health conditions of the coated composite structures have been given high attention, as they are critically important for assessing operational safety and residual service life. However, the current problem is the lack of an efficient, long-term, and stable monitoring technique to characterize the structural behavior of coated composite structures in the whole life cycle. For this reason, bare and packaged fiber Bragg grating (FBG) sensors have been specially developed and designed in sensing networks to monitor the structural performance of CFRP-coated composite beams under different loads. Some optical fibers have also been inserted in the CFRP laminates to configure the smart CFRP component. Detailed data interpretation has been conducted to declare the strengthening process and effect. Finite element simulation and simplified theoretical analysis have been conducted to validate the experimental testing results and the deformation profiles of steel beams before and after the CFRP coating has been carefully checked. Results indicate that the proposed FBG sensors and sensing layout can accurately reflect the structural performance of the composite beam structure, and the CFRP coating can share partial loads, which finally leads to the downward shift in the centroidal axis, with a value of about 10 mm. The externally bonded sensors generally show good stability and high sensitivity to the applied load and temperature-induced inner stress variation. The study provides a straightforward instruction for the establishment of a structural health monitoring system for CFRP-coated composite structures in the whole life cycle. Full article

Background: Urinary tract infections (UTIs) are among the most common types of infections during childhood. Limited data are available on the prevalence of UTI in children from Romania, with most being available for hospitalized children. For this reason, we conducted a retrospective observational study in consecutive non-hospitalized children to assess the number of positive UTI samples and the antibacterial resistance of causative pathogens. Methods: This study included 7222 consecutive urine cultures collected from children aged 1 to 18 years who are residents of Arad County, Western Romania. Urine samples were analyzed for leukocyturia and cultures for the presence of monomorphic bacteria. Results: The overall number of positive UTI samples was 10.44%. A higher number of positive UTI samples was observed in females when compared to males and in children aged 6–12 and 12 to 18 years when compared to those aged 1–5 years. The antibiotic susceptibility testing of E. coli isolates revealed high sensitivity to most tested antibacterials. Near-complete susceptibility was observed for fosfomycin (99.71%) and nitrofurantoin (96.01%), while high susceptibility rates were also observed for ciprofloxacin (85.43%) and amoxicillin–clavulanic acid (75.05%). In contrast, high resistance was found for ampicillin (62.28% resistant) and trimethoprim–sulfamethoxazole (36.53% resistant). Conclusions: Given the clinical risks associated with UTI in children, our findings underscore the urgent need for the continued monitoring of multidrug-resistant strains. Our study provides important epidemiological and resistance data to guide empirical treatment and strengthen pediatric antimicrobial resistance surveillance. Future studies should focus on different regions and regularly update resistance patterns to keep treatment and prevention strategies aligned with local conditions. Full article

A vision-based lane detection system is difficult to implement in vehicles due to the unique characteristics of algorithms, neural network architectures, limitations, and strict hardware/software requirements. This paper proposes a new architecture, called hybrid U-Net, which represents a significant extension of the classical U-Net model, addressing its limitations in capturing global relationships, adaptability to scale variations, and sensitivity to geometric structures. An important aspect of this new architecture is the balance between performance and computational efficiency, maintaining reasonable values of performance parameters with a competitive inference time. The experimental results show that hybrid U-Net achieved superior scores in both mIoU and F1 score parameters. The most notable increase was observed in the (TuSimple+Carla) dataset, where mIoU increased from 0.56 to 0.60 (+7%), and F1 score from 0.69 to 0.74 (+7.2%). Similarly, in the Carla dataset (known for the complexity of the generated images), a substantial improvement was recorded, with an increase of +8.0% in mIoU and +5.7% in F1 score, showing better adaptability of the model to geometric structures in complex images. However, these improvements come at a considerable computational cost, as the inference time of the hybrid U-Net model is approximately 0.04 s per image (compared to 0.016 s per image for U-Net), resulting in a decrease in FPS from 60 to approximately 23. Future research should investigate model compression techniques, such as knowledge distillation, pruning, or quantization, to improve efficiency without sacrificing accuracy. Full article

Ammonia, as a toxic and corrosive gas, is widely present in industrial emissions, agricultural activities, and disease biomarkers. Detecting ammonia is of vital importance to environmental safety and human health. Sensors based on MXene have become an effective means for detecting ammonia gas due to their unique hierarchical structure, adjustable surface chemical properties, and excellent electrical conductivity. This study reviews the latest progress in the use of MXene and its composites for the low-temperature detection of ammonia gas. The strategies for designing MXene composites, including heterojunction engineering, surface functionalization, and active sites, are introduced, and their roles in improving sensing performance are clarified. These methods have significantly improved the ability to detect ammonia, offering high selectivity, rapid responses, and ultra-low detection limits within the low-temperature range. Successful applications in fields such as industrial safety, food quality monitoring, medical diagnosis, and agricultural management have demonstrated the multi-functionality of this technology in complex scenarios. The challenges related to the material’s oxidation resistance, humidity interference, and cross-sensitivity are also discussed. This study aims to briefly describe the reasonable design based on MXene sensors, aiming to achieve real-time and energy-saving environmental and health monitoring networks in the future. Full article

A longstanding ambiguity surrounds the operationalization of consciousness in artificial systems, complicated by the philosophical and cultural weight of subjective experience. This work examines whether cognitive architectures may be designed to support a functionally explicit form of artificial consciousness, focusing not on the replication of phenomenology, but rather on measurable, technically realizable introspective mechanisms. Drawing on a critical review of foundational and contemporary literature, this study articulates a conceptual and methodological shift: from investigating the experiential perspective of agents (“what it is like to be a bat”) to analyzing the informational, self-regulatory, and adaptive structures that enable purposive behavior. The approach combines theoretical analysis with a comparative review of major cognitive architectures, evaluating their capacity to implement access consciousness and internal monitoring. Findings indicate that several state-of-the-art systems already display core features associated with functional consciousness—such as self-explanation, context-sensitive adaptation, and performance evaluation—without invoking subjective states. These results support the thesis that cognitive engineering may progress more effectively by focusing on operational definitions of consciousness that are amenable to implementation and empirical validation. In conclusion, this perspective enables the development of artificial agents capable of autonomous reasoning and self-assessment, grounded in technical clarity rather than speculative constructs. Full article

Background/Objectives: Manual therapy techniques, including mobilization and manipulation, are commonly used for chronic low back pain (CLBP), with clinical guidelines recommending their use. This study aimed to compare the immediate hypoalgesic effects of mobilization and manipulation in patients with non-specific CLBP, evaluating their impact on pain sensitivity and range of motion. Methods: A cross-over randomized controlled trial was conducted with 27 participants with non-specific CLBP. Participants received either mobilization or manipulation on two different intervention days. Outcome measures included pressure pain thresholds (PPTs) assessed with a digital algometer, pain intensity using a numeric rating scale, and lumbar range of motion (ROM) measured with a digital inclinometer. Results: The results indicated no statistically significant differences between mobilization and manipulation for any outcome measures (all p > 0.05). However, significant within-intervention improvements were observed, including pain reduction, increased PPTs, and enhanced ROM of the lower back. Conclusions: Our findings suggest that both mobilization and manipulation provide similar immediate benefits for patients with CLBP. The choice between these techniques should be based on therapists’ clinical reasoning and individualized risk stratification, considering the potential benefits and risks of each approach for a specific patient. Full article